Automated retinal microvascular quantification as a predictor of cardiovascular disease risk in later life

眼底照相机很容易对眼睛后部的视网膜血管（包括动脉和静脉）进行成像。视网膜血管（特别是动脉）的形状和大小与晚年患心血管疾病的风险有关，可能为高风险个体提供有价值的预测。最近的研究还表明，视网膜血管的大小和形状与早期心血管危险因素（包括血压和血胆固醇）有关，这表明这些措施可能是血管健康的非侵入性标志物。成像和计算方法的进步为越来越精确地成像和分析视网膜血管提供了机会。然而，到目前为止，这些方法还不是完全自动化的（相对于相关数据集），并且严重依赖于操作员的参与，特别是区分动脉和静脉（它们可能会因疾病而改变大小和形状）。这些问题限制了视网膜成像在老年心血管疾病和其他关键慢性疾病的大规模人群研究中的应用，特别是眼部疾病（黄斑变性和青光眼）和2型糖尿病。该提案旨在在中年人（包括UK Biobank - UKBB）的大型研究中全自动识别和表征视网膜图像中的血管，包括动脉和静脉的识别。图像（来自超过70,000名参与者的近150,000张图像）将用于改进我们现有的测量视网膜血管大小和形状的方法（包括血管宽度和扭曲度的测量，即扭曲/转动的测量），并检查它们与晚年心血管疾病风险的关系。家庭医生经常给中年人看病，并根据一些简单的指标（如年龄、性别、血压和血胆固醇）给他们心血管疾病风险评分（即心脏病发作风险）；这通常决定是否治疗（例如，降压药物，他汀类药物）。该项目汇集了一个完善的多学科小组，共同的目标是开发可靠，自动化和高效的视网膜图像分析软件，该软件是免费提供的，在大量视网膜图像中生成丰富的视网膜血管特征。通过在小组内纳入适当的统计专门知识，将确保创新地使用从数千名参与者产生的大规模数据。目前正在使用小组内开发的软件进行图像分析，本提案旨在将软件开发成所有人都可以使用的可用形式。该项目的潜在科学价值是可观的：(1)筛选：视网膜血管特征可以提供异常的测量方法，预测血管整体健康状况和晚年严重血管疾病。除了现有的筛查工具外，这可以识别需要早期治疗的“高危人群”，从而减少与心血管事件相关的并发症。(2)新资源：生成的经过验证的视网膜测量值本身将成为这些队列的数据，使更广泛的科学界能够进行进一步的研究。(3)协作：专家小组的结合有望在这些软件工具的性能上产生一个阶梯式的改进。(4)通过将小组开发的软件提供给公众使用，保证了工作的长期科学价值。这确保了检查视网膜血管作为疾病风险和结果（循环系统和眼部疾病）预测因子的价值被尽可能广泛的科学界实现。

Retinal vessels (both arteries and veins) on the back of the eye are easily imaged using fundus cameras. The shape and size of retinal vessels (particularly arteries), have been related to risk of cardiovascular disease in later life and may provide valuable prediction of individuals at high risk of disease. Recent studies have also shown that retinal vessel size and shape are associated with cardiovascular risk factors (including blood pressure and blood cholesterol) from an early age, suggesting that these measures may be non-invasive markers of the health of blood vessels. Advances in imaging and computational methods are providing opportunities to image and analyse retinal vessels with increasing precision. However, to date these methods are not fully automated (with respect to the relevant datasets) and rely heavily on operator involvement, especially to distinguish arteries from veins (which may change in size and shape differently in response to disease). These problems limit the application of retinal imaging in large population studies of cardiovascular disease and other key chronic diseases in later life, particularly ocular disease (macular degeneration and glaucoma) and type 2 diabetes. This proposal aims to fully automate identification and characterisation of vessels in retinal images, including identification of arteries and veins, in large studies of middle aged adults (including UK Biobank - UKBB). Images (nearly 150,000 images from over 70,000 participants) will be used to refine our existing approaches to measurement of retinal vessel size and shape (which include both measures of vessel width and tortuosity, i.e., a measure of twisting / turning), and to examine their association with risk of cardiovascular disease in later life. Middle age adults are often seen by family doctors and given a risk score for cardiovascular disease (i.e., risk of heart attack), based on a number of simple measures (e.g., age, gender, blood pressure and blood cholesterol); this often determines whether treatments (e.g., BP medications, statins) are prescribed. The project brings together a well-established, multidisciplinary group with the shared aim of developing reliable, automated and efficient retinal image analysis software, which is freely available, generating a rich characterization of the retinal vessels in large numbers retinal images. Innovative use of the large-scale data generated from thousands of participants, will be ensured by the inclusion of appropriate statistical expertise within the group. Current image analysis is underway using software developed within the group, and this proposal seeks to develop the software into a usable form available to all. The potential scientific value of the project is appreciable: (1) Screening: retinal vessel characteristics may provide measures of abnormality that are predictors of overall vascular health and serious vascular disease in later life. In addition to established screening tools, this may identify "at-risk groups" in need of earlier treatment and therefore reduce the complications associated with cardiovascular events. (2) New resources: the validated retinal measurements generated will become themselves data to add to these cohorts, enabling further investigation by the wider scientific community. (3) Collaboration: Combination of specialist groups is expected to generate a step-change improvement in performance of these software tools. (4) Long-term scientific value of the work is assured by making the software developed by the group, available for public use. This ensures that the value of examining retinal vessels as a predictor of disease risk and outcome (both circulatory and ocular disease) is realised by the widest possible scientific community.